KEIO RESEARCHERS INFORMATION SYSTEM

Profile Summary 【 Display / hide 】

Profile Summary 【 Display / hide 】

• ・1999年04月-2001年03月 早稲田大学理工学部　応用物理学科助手 　橋本周司教授の研究室において、顔画像認識・合成、工業用精密画像計測、 　ヒューマノイドロボットの視覚システムに関する研究に従事． ・2002年04月-2005年03月 芝浦工業大学工学部情報工学科 専任講師（青木研究室発足） 　2005年04月-2008年3月 芝浦工業大学工学部情報工学科　准教授 　顔形状・動作の３次元画像解析技術の医学・歯学応用 　衛星画像他リモートセンシングデータの統合活用に関する研究 　道路交通画像システム，高精度画像計測システムに関する研究等に従事． 　※芝浦工業大学にて、７年間で約９０名の学生の研究指導を担当 ・2008年04月-現在　慶應義塾大学理工学部電子工学科　准教授 　人物を対象とした画像計測・認識技術、及び応用システムに関する研究． 　応用先として，セキュリティ，マーケティング，医療・福祉，美容，インターフェース，エンターテイメント，自動車，等を視野に入れ，幅広い産業応用を目指す． 　人の認知機構や感性を考慮したメディア理解技術とその応用，新しい視覚センサ，ロバスト画像特徴量に関する研究等に従事． ・2013年2月-現在　株式会社イデアクエスト　取締役兼任 　慶應理工発画像センシング技術の医療分野での実用化を目指している．

Career 【 Display / hide 】

Career 【 Display / hide 】

• 1999.04

  -

  2002.03

  早稲田大学, 理工学部　, 助手

• 2002.04

  -

  2005.03

  芝浦工業大学　, 工学部　情報工学科, 専任講師

• 2005.04

  -

  2008.03

  芝浦工業大学, 工学部　情報工学科, 助教授（2007より准教授）

• 2008.04

  -

  2017.03

  慶應義塾大学, 理工学部, 准教授

• 2013.02

  -

  2017.03

  株式会社イデアクエスト, 取締役

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Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 1996.03

  Waseda University, Faculty of Science and Engineering, 応用物理学科

  University, Graduated

• 1998.03

  Waseda University, Graduate School, Division of Science and Engineering, 物理学及応用物理学専攻

  Graduate School, Completed, Master's course

• 2001.02

  Waseda University, Graduate School, Division of Science and Engineering, 物理学及応用物理学専攻

  Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• 博士（工学）, Waseda University, Coursework, 2001.02

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Measurement engineering (Measurement Engineering)

• Informatics / Database (Media Informatics/Data Base)

• Informatics / Perceptual information processing (Perception Information Processing/Intelligent Robotics)

• Life Science / Medical systems (Medical Systems)

Books 【 Display / hide 】

Books 【 Display / hide 】

• 画像センシングのしくみと開発がしっかりわかる教科書

  青木義満，輿水大和 他, 技術評論社, 2023.06,  Page： 239

• 顔の百科事典

  丸善出版, 2015.09

  Scope: 7 章 コンピュータと顔 ─顔の情報学─

  　View Summary

  顔を見ない日はないというくらい、「顔」は私達にとってあたり前の存在ですが、私達は一体どれほど「顔」のことを知っているのでしょうか。そのような「顔」を総合的に研究するのが「顔学」です。 顔学には、動物学や人類学をはじめ、解剖学、生理学、歯学、心理学、社会学の文化的な対象として扱われるだけでなく、演劇や美術などの芸術学、コンピュータの分野では、情報学、さらに、美容学、人相学など、実に多様な学問分野と関係しています。 本書では、私達と切り離すことのできない「顔」の、歴史的・文化的・社会的・科学的側面を中項目の事典としてまとめられていることにより、多様な分野を横断する知識にも容易にアクセスが可能になっています。 日本顔学会創立20周年記念出版として、「顔学」について体系化を行った、初めての百科事典です。

• 三次元画像センシングの新展開

  AOKI Yoshimitsu, NTS, 2015.05

  Scope: 第5章1節　色情報とレンジデータのフュージョンによる高分解能三次元レンジセンサの開発

• 電気学会125年史

  AOKI Yoshimitsu, 電気学会, 2013.05

• 電気学会125年史

  AOKI Yoshimitsu, 電気学会, 2013.05

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Papers 【 Display / hide 】

Papers 【 Display / hide 】

• DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios

  Sun Y., Qiu Y., Aoki Y.

  Sensors 25 ( 2 )  2025.01

  　View Summary

  Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent’s decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation. To bridge this gap, we propose a novel task called Dynamic Vision-and-Language Navigation (DynamicVLN), incorporating various dynamic scenarios to enhance the agent’s decision-making abilities and adaptability. By redefining the VLN task, we emphasize that a robust and generalizable agent should not rely solely on predefined instructions but must also demonstrate reasoning skills and adaptability to unforeseen events. Specifically, we have designed ten scenarios that simulate the challenges of dynamic navigation and developed a dedicated dataset of 11,261 instances using the CARLA simulator (ver.0.9.13) and large language model to provide realistic training conditions. Additionally, we introduce a baseline model that integrates advanced perception and decision-making modules, enabling effective navigation and interpretation of the complexities of dynamic road conditions. This model showcases the ability to follow natural language instructions while dynamically adapting to environmental cues. Our approach establishes a benchmark for developing agents capable of functioning in real-world, dynamic environments and extending beyond the limitations of static VLN tasks to more practical and versatile applications.

  • Access to Document (DOI)

• A Comprehensive Analysis of a Social Intelligence Dataset and Response Tendencies Between Large Language Models (LLMs) and Humans

  Mori E., Qiu Y., Kataoka H., Aoki Y.

  Sensors 25 ( 2 )  2025.01

  　View Summary

  In recent years, advancements in the interaction and collaboration between humans and have garnered significant attention. Social intelligence plays a crucial role in facilitating natural interactions and seamless communication between humans and Artificial Intelligence (AI). To assess AI’s ability to understand human interactions and the components necessary for such comprehension, datasets like Social-IQ have been developed. However, these datasets often rely on a simplistic question-and-answer format and lack justifications for the provided answers. Furthermore, existing methods typically produce direct answers by selecting from predefined choices without generating intermediate outputs, which hampers interpretability and reliability. To address these limitations, we conducted a comprehensive evaluation of AI methods on a video-based Question Answering (QA) benchmark focused on human interactions, leveraging additional annotations related to human responses. Our analysis highlights significant differences between human and AI response patterns and underscores critical shortcomings in current benchmarks. We anticipate that these findings will guide the creation of more advanced datasets and represent an important step toward achieving natural communication between humans and AI.

  • Access to Document (DOI)

• Data Collection-Free Masked Video Modeling

  Ishikawa Y., Kondo M., Aoki Y.

  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 15069 LNCS   37 - 56 2025

  ISSN  03029743

  　View Summary

  Pre-training video transformers generally requires a large amount of data, presenting significant challenges in terms of data collection costs and concerns related to privacy, licensing, and inherent biases. Synthesizing data is one of the promising ways to solve these issues, yet pre-training solely on synthetic data has its own challenges. In this paper, we introduce an effective self-supervised learning framework for videos that leverages readily available and less costly static images. Specifically, we define the Pseudo Motion Generator (PMG) module that recursively applies image transformations to generate pseudo-motion videos from images. These pseudo-motion videos are then leveraged in masked video modeling. Our approach is applicable to synthetic images as well, thus entirely freeing video pre-training from data collection costs and other concerns in real data. Through experiments in action recognition tasks, we demonstrate that this framework allows effective learning of spatio-temporal features through pseudo-motion videos, significantly improving over existing methods which also use static images and partially outperforming those using both real and synthetic videos. These results uncover fragments of what video transformers learn through masked video modeling.

  • Access to Document (DOI)

• Rethinking Image Super-Resolution from Training Data Perspectives

  Ohtani G., Tadokoro R., Yamada R., Asano Y.M., Laina I., Rupprecht C., Inoue N., Yokota R., Kataoka H., Aoki Y.

  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 15075 LNCS   19 - 36 2025

  ISSN  03029743

  　View Summary

  In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectives of diversity and quality, thereby addressing the question of “How important is SR training for SR models?”. To this end, we propose an automated image evaluation pipeline. With this, we stratify existing high-resolution image datasets and larger-scale image datasets such as ImageNet and PASS to compare their performances. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance. We hope that the proposed simple-yet-effective dataset curation pipeline will inform the construction of SR datasets in the future and yield overall better models. Code is available at: https://github.com/gohtanii/DiverSeg-dataset.

  • Access to Document (DOI)

• Unsupervised Metric Learning for Expressing Color and Shape Information to Uncover Abstract Connections within Image Datasets

  Obikane S., Tagawa H., Aoki Y.

  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 15321 LNCS   15 - 30 2025

  ISSN  03029743

  　View Summary

  In this research, we propose a novel approach using unsupervised metric learning tailored to datasets characterized by complex similarities and connections, such as those found in paintings and makeup, which are challenging to express linguistically. These datasets often present the difficulty of adequately analyzing data points due to the intricate interplay of defining elements, a limitation of traditional labeling methods. Additionally, the high degree of specialization required makes annotation significantly costly. Unsupervised metric learning emerges as a powerful method for extracting more cost-effective features and for the comprehensive analysis of these datasets. Expanding upon previous research that utilized style transfer models, our study further explores feature design, specifically focusing on extracting detailed information about critical aspects of similarity assessment, such as color and shape. Our model adeptly incorporates visual information, unveiling the hidden abstract connections within datasets. We validated our approach using a dataset of Ukiyo-e, a genre of Japanese painting, and achieved accuracy comparable to supervised learning models. This research opens up new possibilities for the analysis of complex image datasets with abstract relational depth, fostering a deeper understanding of the data.

  • Access to Document (DOI)

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Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

• Construction of feature extraction method for turbulence big data by machine learning

  Aoki, Yoshimitsu

  科学研究費補助金研究成果報告書 2020

• Gaze control and dexterous manipulation of infants' goal-directed actions : a comparison between 11 month olds and 18 month olds

  Aoki, Yoshimitsu

  Studies in sociology, psychology and education : inquiries into humans and societies (慶應義塾大学大学院社会学研究科)   ( 82 ) 17 - 35 2016

  ISSN  0912456X

• 産学連携のカタチ

  Aoki, Yoshimitsu

  新版 窮理図解 (慶應義塾大学理工学部)   ( 15 )  2014.01

• The way industry-and-academia collaboration is conducted

  Aoki, Yoshimitsu

  New Kyurizukai (Faculty of Science and Technology, Keio University)   ( 15 )  2014.01

Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

• 密集領域での動作を理解するためのハイブリッド型映像解析

  大内一成，小林大祐，中州俊信，青木義満

  東芝レビュー (東芝)  72 ( 4 ) 30 - 34 2017.09

  Internal/External technical report, pre-print, etc., Joint Work

• 画像センシング技術によるチームスポーツ映像からのプレー解析

  林　昌希，青木　義満

  映像情報メディア学会誌 (映像情報メディア学会)  70 ( 5 ) 710 - 714 2016.09

  Article, review, commentary, editorial, etc. (scientific journal), Joint Work

• Image Sensing Technologies and its Applications for Human Action Recognition

  AOKI Yoshimitsu

  Journal of JSNDI (日本非破壊検査協会)  65 ( 6 ) 254 - 260 2016.06

  Article, review, commentary, editorial, etc. (scientific journal), Single Work

• パターン計測技術の深化と広がる産業応用 -総論-

  AOKI Yoshimitsu

  計測と制御 (SICE)  53 ( 7 ) 555 - 556 2014.07

  Article, review, commentary, editorial, etc. (scientific journal), Single Work

Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

• 不確実性を考慮したセマンティックマップの生成

  竹中悠，森巧磨，谷口恭弘，青木義満

  第27回 知能メカトロニクスワークショップ, 

  2022.09

  , 

  Oral presentation (general)

• 自由な表現と被写体の質感を維持するメイク生成モデルの開発

  帯金駿, 田川晴菜, 中川雄介, 中村理恵, 青木義満

  第27回日本顔学会大会（フォーラム顔学2022）, 

  2022.09

  , 

  Oral presentation (general)

• 重要パッチ選択に基づく効率的動画認識

  鈴木 智之, 青木 義満

  第25回 画像の認識・理解シンポジウム（MIRU2022）, 

  2022.07

  , 

  Poster presentation

• 音響信号を用いた人物の3次元姿勢推定

  川島穣, 柴田優斗, 五十川麻理子, 入江豪, 木村昭悟, 青木義満

  第25回 画像の認識・理解シンポジウム（MIRU2022）, 

  2022.07

  , 

  Oral presentation (general)

• 完全合成画像での学習による文書画像の影除去

  松尾祐飛，青木義満

  第28回画像センシングシンポジウム（SSII2022）, 

  2022.06

  , 

  Poster presentation

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Intellectual Property Rights, etc. 【 Display / hide 】

Intellectual Property Rights, etc. 【 Display / hide 】

• 画像処理装置，画像処理プログラムおよび画像処理方法

  Date applied： 2019-105297  2019.06 

  Joint

• 危険度推定装置，危険度推定方法及び危険度推定用コンピュータプログラム

  Date applied： 特願2015-005241  2015.01 

  Date issued： 特許第6418574号  2018.10

  Patent, Joint

Awards 【 Display / hide 】

Awards 【 Display / hide 】

• HCGシンポジウム2018 特集テーマセッション賞

  秋月 秀一(慶大)・大木 美加・バティスト ブロー・鈴木 健嗣(筑波大)・青木 義満(慶大), 2018.12, 電子情報通信学会ヒューマンコミュニケーショングループ, 床面プロジェクションに伴う動的な環境変化に対応する人物追跡技術

  Type of Award： Award from Japanese society, conference, symposium, etc.

• HCGシンポジウム2018 優秀インタラクティブ発表賞

  秋月 秀一(慶大)・大木 美加・バティスト ブロー・鈴木 健嗣(筑波大)・青木 義満(慶大), 2018.12, 電子情報通信学会ヒューマンコミュニケーショングループ, 床面プロジェクションに伴う動的な環境変化に対応する人物追跡技術

  Type of Award： Award from Japanese society, conference, symposium, etc.

• 精密工学会沼田記念論文賞

  加藤直樹，箱崎浩平，里雄二，古山純子，田靡雅基，青木ヨシミツ, 2018.03, 精密工学会, 畳み込みニューラルネットワークによる距離学習を用いた動画像人物再同定

  Type of Award： Award from Japanese society, conference, symposium, etc.

• IWAIT2018 Best Paper Award

  Ryunosuke Kurose, Masaki Hayashi, Yoshimitsu Aoki, 2018.01, IWAIT2018

  Type of Award： International academic award (Japan or overseas)

• IES-KCIC2017 Best Paper Award

  Siti Nor Khuzaimah Amit, Yoshimitsu Aoki, 2017.09, IEEE Indonesia Section, Disaster Detection from Aerial Imagery with Convolutional Neural Network

  Type of Award： International academic award (Japan or overseas)

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Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• SEMINAR IN ELECTRONICS AND INFORMATION ENGINEERING(2)

  2025

• SEMINAR IN ELECTRONICS AND INFORMATION ENGINEERING(1)

  2025

• RECITATION IN ELECTRONICS AND INFORMATION ENGINEERING

  2025

• LABORATORIES IN ELECTRONICS AND INFORMATION ENGINEERING(2)

  2025

• INDEPENDENT STUDY ON INTEGRATED DESIGN ENGINEERING

  2025

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Social Activities 【 Display / hide 】

Social Activities 【 Display / hide 】

• 画像情報教育振興協会

  2013.07

  -

  2015.03

• 独立行政法人 交通安全環境研究所

  2009.12

  -

  2012.03

Memberships in Academic Societies 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

• International Symposium on Optomechatronic Technologies 2013, 

  2013.04

  -

  2013.11

• International Workshop on Advanced Image Technology 2013(IWAIT2013), 

  2013.01

  -

  2013.09

• 11th International Conference on Quality Control by Artificial Vision(QCAV2013), 

  2012.12

  -

  2013.05

• 3rd International Conference on 3D Body Scanning Technologies, 

  2012.06

  -

  2012.10

• 計測自動制御学会パターン計測部会, 

  2012.04

  -

  Present

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Committee Experiences 【 Display / hide 】

Committee Experiences 【 Display / hide 】

• 2017.04

  -

  Present

  NEDO技術委員, NEDO

• 2016.07

  -

  2016.11

  Optics & Photonics Japan 2016　推進委員, 日本光学会

• 2016.07

  -

  2016.12

  Program committee member, International Workshop on Human Tracking and Behavior Analysis 2016

• 2015.09

  -

  2016.08

  第22回画像センシングシンポジウム　実行委員長, 画像センシング技術研究会

• 2014.09

  -

  2015.08

  第21回画像センシングシンポジウム　実行委員長, 画像センシング技術研究会

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